22,190 research outputs found
Sequential Sensing with Model Mismatch
We characterize the performance of sequential information guided sensing,
Info-Greedy Sensing, when there is a mismatch between the true signal model and
the assumed model, which may be a sample estimate. In particular, we consider a
setup where the signal is low-rank Gaussian and the measurements are taken in
the directions of eigenvectors of the covariance matrix in a decreasing order
of eigenvalues. We establish a set of performance bounds when a mismatched
covariance matrix is used, in terms of the gap of signal posterior entropy, as
well as the additional amount of power required to achieve the same signal
recovery precision. Based on this, we further study how to choose an
initialization for Info-Greedy Sensing using the sample covariance matrix, or
using an efficient covariance sketching scheme.Comment: Submitted to IEEE for publicatio
Info-Greedy sequential adaptive compressed sensing
We present an information-theoretic framework for sequential adaptive
compressed sensing, Info-Greedy Sensing, where measurements are chosen to
maximize the extracted information conditioned on the previous measurements. We
show that the widely used bisection approach is Info-Greedy for a family of
-sparse signals by connecting compressed sensing and blackbox complexity of
sequential query algorithms, and present Info-Greedy algorithms for Gaussian
and Gaussian Mixture Model (GMM) signals, as well as ways to design sparse
Info-Greedy measurements. Numerical examples demonstrate the good performance
of the proposed algorithms using simulated and real data: Info-Greedy Sensing
shows significant improvement over random projection for signals with sparse
and low-rank covariance matrices, and adaptivity brings robustness when there
is a mismatch between the assumed and the true distributions.Comment: Preliminary results presented at Allerton Conference 2014. To appear
in IEEE Journal Selected Topics on Signal Processin
Reconfigurable nanoelectronics using graphene based spintronic logic gates
This paper presents a novel design concept for spintronic nanoelectronics
that emphasizes a seamless integration of spin-based memory and logic circuits.
The building blocks are magneto-logic gates based on a hybrid
graphene/ferromagnet material system. We use network search engines as a
technology demonstration vehicle and present a spin-based circuit design with
smaller area, faster speed, and lower energy consumption than the
state-of-the-art CMOS counterparts. This design can also be applied in
applications such as data compression, coding and image recognition. In the
proposed scheme, over 100 spin-based logic operations are carried out before
any need for a spin-charge conversion. Consequently, supporting CMOS
electronics requires little power consumption. The spintronic-CMOS integrated
system can be implemented on a single 3-D chip. These nonvolatile logic
circuits hold potential for a paradigm shift in computing applications.Comment: 14 pages (single column), 6 figure
Evolution of new regulatory functions on biophysically realistic fitness landscapes
Regulatory networks consist of interacting molecules with a high degree of
mutual chemical specificity. How can these molecules evolve when their function
depends on maintenance of interactions with cognate partners and simultaneous
avoidance of deleterious "crosstalk" with non-cognate molecules? Although
physical models of molecular interactions provide a framework in which
co-evolution of network components can be analyzed, most theoretical studies
have focused on the evolution of individual alleles, neglecting the network. In
contrast, we study the elementary step in the evolution of gene regulatory
networks: duplication of a transcription factor followed by selection for TFs
to specialize their inputs as well as the regulation of their downstream genes.
We show how to coarse grain the complete, biophysically realistic
genotype-phenotype map for this process into macroscopic functional outcomes
and quantify the probability of attaining each. We determine which evolutionary
and biophysical parameters bias evolutionary trajectories towards fast
emergence of new functions and show that this can be greatly facilitated by the
availability of "promiscuity-promoting" mutations that affect TF specificity
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